Overview

A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.

Full Product Details

Author:   Nailong Zhang
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Weight:   0.408kg
ISBN:  

9780367895860

ISBN 10:   0367895862
Pages:   216
Publication Date:   12 November 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Assumptions about the reader’s background Book overview Introduction to R/Python Programming Calculator Variable and Type Functions Control flows Some built-in data structures Revisit of variables Object-oriented programming (OOP) in R/Python Miscellaneous More on R/Python Programming Work with R/Python scripts Debugging in R/Python Benchmarking Vectorization Embarrassingly parallelism in R/Python Evaluation strategy Speed up with C/C++ in R/Python A first impression of functional programming Miscellaneous data.table and pandas SQL Get started with data.table and pandas Indexing & selecting data Add/Remove/Update Group by Join Random Variables, Distributions & Linear Regression A refresher on distributions Inversion sampling & rejection sampling Joint distribution & copula Fit a distribution Confidence interval Hypothesis testing Basics of linear regression Ridge regression Optimization in Practice Convexity Gradient descent Root-finding General purpose minimization tools in R/Python Linear programming Miscellaneous Machine Learning - A gentle introduction Supervised learning Gradient boosting machine Unsupervised learning Reinforcement learning Deep Q-Networks Computational differentiation Miscellaneous

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Author Information

Nailong Zhang is lead Data Scientist at Mass Mutual Life Insurance Company.

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